Development and application of a new algorithm for automated pit removal for grid DEMs

Ao, Tianqi; Takeuchi, Kuniyoshi; Ishidaira, Hiroshi; Yoshitani, Junich; Fukami, Kimio

doi:10.1623/hysj.48.6.985.51423

Cited by 32 publications

(11 citation statements)

References 20 publications

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“…The horizontal and vertical accuracy is 0Ð3 m and 0Ð15 m, respectively. The DEM sinks are evaluated by the fill-sink-routine (Jenson and Domingue, 1988;Tianqi et al, 2003) that is implemented in ArcGIS9 (ESRI, 1997). The algorithm increases the elevation of all grid cells within a sink to the height of the pour point of the sink.…”

Section: Incorporating Landscape Depressions-setupmentioning

confidence: 99%

Incorporating landscape depressions and tile drainages of a northern German lowland catchment into a semi‐distributed model

Kiesel

Fohrer

Schmalz

et al. 2010

Hydrological Processes

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Abstract:Hydrological models need to be adapted to specific hydrological characteristics of the catchment in which they are applied. In the lowland region of northern Germany, tile drains and depressions are prominent features of the landscape though are often neglected in hydrological modelling on the catchment scale. It is shown how these lowland features can be implemented into the Soil and Water Assessment Tool (SWAT). For obtaining the necessary input data, results from a GIS method to derive the location of artificial drainage areas have been used. Another GIS method has been developed to evaluate the spatial distribution and characteristics of landscape depressions. In the study catchment, 31% of the watershed area is artificially drained, which heavily influences groundwater processes. Landscape depressions are common over the 50-km 2 study area and have considerable retention potential with an estimated surface area of 582 ha. It was the scope of this work to evaluate the extent by which these two processes affect model performance. Accordingly, three hypotheses have been formulated and tested through a stepwise incorporation of drainage and depression processes into an auto calibrated default setup: (1) integration of artificial drainage alone; (2) integration of depressions alone and (3) integration of both processes combined. The results show a strong improvement of model performance for including artificial drainage while the depression setup only induces a slight improvement. The incorporation of the two landscape characteristics combined led to an overall enhancement of model performance and the strongest improvement in r 2 , root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) of all setups. In particular, summer rainfall events with high intensity, winter flows and the hydrograph's recession limbs are depicted more realistically.

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Section: Incorporating Landscape Depressions-setupmentioning

confidence: 99%

Incorporating landscape depressions and tile drainages of a northern German lowland catchment into a semi‐distributed model

Kiesel

Fohrer

Schmalz

et al. 2010

Hydrological Processes

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“…In karst-depression detection studies a promising terrain attribute is the sink depth derived from the depression-filling algorithm [24][25][26][27]. Such algorithms are an integral component of spatially distributed hydrological models that delineate watersheds, drainage networks and overland flowpaths [28][29][30][31][32]. Other methods to automate karst depression recognition include convolution or filtering with kernel windows using focal functions [33] and the "active-contour" method [34,35], an algorithm that delineates sinkhole boundaries with a compactness test and by fitting a local bi-quadratic surface to the points surrounding the potential sinkhole locations.…”

Section: Introductionmentioning

confidence: 99%

Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil

et al. 2013

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Abstract:Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM-and ALOS/PRISM-derived digital elevation models (DEMs) to detect and quantify natural karst depressions along the Sã o Francisco River near Barreiras city, northeast Brazil. The study area is a karst landscape characterized by karst depressions (dolines), closed depressions in limestone, many of which contain standing water connected with the ground-water table. The base of dolines is typically sealed with an impermeable clay layer covered by standing water or herbaceous vegetation. We identify dolines by combining the extraction of sink depth from DEMs, morphometric analysis using GIS, and visual interpretation. Our methodology is a semi-automatic approach involving several steps: (a) DEM acquisition; (b) sink-depth calculation using the difference between the raw DEM and the corresponding DEM with sinks filled; and (c) elimination of falsely identified karst OPEN ACCESSRemote Sens. 2014, 6 331 depressions using morphometric attributes. The advantages and limitations of the applied methodology using different DEMs are examined by comparison with a sinkhole map generated from traditional geomorphological investigations based on visual interpretation of the high-resolution remote sensing images and field surveys. The threshold values of the depth, area size and circularity index appropriate for distinguishing dolines were identified from the maximum overall accuracy obtained by comparison with a true doline map. Our results indicate that the best performance of the proposed methodology for meso-scale karst feature detection was using ALOS/PRISM data with a threshold depth > 2 m; areas > 13,125 m 2 and circularity indexes > 0.3 (overall accuracy of 0.53). The overall correct identification of around half of the true dolines suggests the potential to substantially improve doline identification using higher-resolution LiDAR-generated DEMs.

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“…DEMs usually show a large number of pits, up to 5% or more of the total number of cells in a given domain [35,36]. Processing the DEM of Italy produced by the Italian Geographic Military Institute (IGMI) at a resolution of 75 m we identified pits covering approximately 1% of the total surface.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Martz and Garbrecht [23,24], and Rieger [32] propose to ''breach'' the depression favouring the flow downstream through the bounding outlet. Tianqi et al [36] propose to tune elevation adjustments in relation to the location of the depression in the basin with the aim of reproducing a more natural channel profile. Soille et al [34] propose to ''carve'' the terrain to enforce convergent flow patterns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A physically-based method for removing pits in digital elevation models

Grimaldi

Nardi

Benedetto

et al. 2007

Advances in Water Resources

101

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Development and application of a new algorithm for automated pit removal for grid DEMs

Cited by 32 publications

References 20 publications

Incorporating landscape depressions and tile drainages of a northern German lowland catchment into a semi‐distributed model

Incorporating landscape depressions and tile drainages of a northern German lowland catchment into a semi‐distributed model

Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil

A physically-based method for removing pits in digital elevation models

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